The model of a three-degree-of-freedom Wave Energy
Converter can be simplified as a linear time-varying system.
In this model, the heave mode parametrically excites the pitch
mode, which in turn excites the surge mode. The heave mode,
however, is independent to the other two modes when the motion
is small. The purpose of this paper is to design a controller to
maximize the energy harvested over a receding time horizon.
We also want to demonstrate that, with proper design of the
control, it is possible to exploit this nonlinear coupling between
the modes so as to harvest more energy. The controller selected is
the linear quadratic Gaussian optimal control. The prediction of
excitation forces is constructed based on the estimation where the
estimations are obtained by using extended Kalman Filter. The
prediction of excitation force is fed into the controller to compute
the time-varying linear quadratic optimal control. Constraints on
the WEC motion are accounted for in computing the control. The
results show that the energy captured by three-degree-of-freedom
Wave Energy Converter is 3:56 times the energy extracted in
heave mode only. Higher energy harvesting is demonstrated when
the linear time-varying model is used in control design.
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Shared Human-Machine Control for Self-Aware Prostheses
This paper presents a framework for shared, human-machine control of a prosthetic arm. The method employs electromyogram and peripheral neural signals to decode motor intent, and incorporates a higher-level goal in the controller to augment human effort. The controller derivation employs Markov Decision Processes. The system is trained using a gradient ascent approach in which the policy is parameterized using a Kalman Filter and the goal is incorporated by adapting the Kalman filter output online. Results of experimental performance analysis of the shared controller when the goal information is imperfect are presented in the paper. These results, obtained from an amputee subject and a subject with intact arms, demonstrate that a system controlled by the human user and the machine together exhibit better performance than systems employing machine-only or human-only control.
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- Award ID(s):
- 1533649
- NSF-PAR ID:
- 10121190
- Date Published:
- Journal Name:
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 6593 to 6597
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP.2018.8461440
